A Novel Architecture based on Deep Learning for Scene Image Recognition

Authors

  • Bhavesh Shri Kumar B.Tech Computer Science and Engineering School of Computing, SASTRA Deemed University, Thanjavur, India. Author
  • J. Naren Assistant Professor, School of Computing, SASTRA Deemed University, Thanjavur, India Author
  • Dr.G. Vithya Professor, School of Computing, KL University, Vijayawada Author
  • K. Prahathish B.Tech Computer Science and Engineering, School of Computing, SASTRA Deemed University, Thanjavur, India. Author

DOI:

https://doi.org/10.61841/t5fynz19

Keywords:

Scene Recognition, Artificial Neural Networks, Deep Neural networks, Convoluted Neural Networks.

Abstract

Scene recognition is a work of great significance in computer vision and artificial intelligence. Certainly, it is difficult due to various factors like cluttered image, poor separation of boundaries in between the scene objects, bad lighting, etc. Hence, the topic receives huge research attention. In this paper, the various applications using Scene Recognition and the various techniques that are incorporated to classify, feature extract and cluster scene images are reviewed and a method based on DNN is proposed. 

Downloads

Download data is not yet available.

References

[1] Y. Leng et al., “Audio scene recognition based on audio events and topic model,” Knowledge-Based Syst.,

vol. 125, pp. 1–12, 2017.J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. Oxford:

Clarendon, 1892, pp.68–73.

[2] Z. Zhang, Z. He, G. Cao, and W. Cao, “Animal Detection from Highly Cluttered Natural Scenes Using

Spatiotemporal Object Region Proposals and Patch Verification,” IEEE Trans. Multimed., vol. 18, no. 10,

pp. 2079–2092, 2016.K. Elissa, “Title of paper if known,” unpublished.

[3] J. Li, X. Mei, D. Prokhorov, and D. Tao, “Deep Neural Network for Structural Prediction and Lane

Detection in Traffic Scene,” IEEE Trans. Neural Networks Learn. Syst., vol. 28, no. 3, pp. 690–703, 2017..

[4] Su and S. Lu, “Accurate recognition of words in scenes without character segmentation using recurrent

neural network,” Pattern Recognit., vol. 63, no. June 2016, pp. 397–405, 2017.

[5] T. He, W. Huang, Y. Qiao, and J. Yao, “Text-Attentional Convolutional Neural Networks for Scene Text

Detection,” arXiv Pre- print, vol. 25, no. 6, pp. 1– 10, 2015.

[6] Payne and S. Singh, “Indoor vs. outdoor scene classification in digital photographs,” Pattern Recognit., vol.

38, no. 10, pp. 1533– 1545, 2005.

[7] J. Gao, J. Yang, G. Wang, and M. Li, “A novel feature extraction method for scene recognition based on

Centered Convolutional Restricted Boltzmann Machines,” Neurocomputing, vol. 214, pp. 708– 717, 2016.

[8] J. Yao, T. Chicago, S. Fidler, and R. Urtasun, “Describing the Scene as a Whole: Joint Object Detection,

Scene Classification and Semantic Segmentation,” 2012.

[9] Cheng, A. Koschan, C. H. Chen, D. L. Page, and M. A. Abidi, “Outdoor scene image segmentation based

on background recognition and perceptual organization,” IEEE Trans. Image Process., vol. 21, no. 3, pp.

1007– 1019, 2012.

[10] Y. Yuan, L. Mou, and X. Lu, “Scene Recognition by Manifold Regularized Deep Learning Architecture,”

IEEE Trans. Neural Networks Learn. Syst., vol. 26, no. 10, pp. 2222–2233, 2015.

[11] Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva, “Learning Deep Features for Scene Recognition

using Places Database,” Adv. Neural Inf. Process. Syst. 27, pp. 487–495, 2014.

[12] J. Sun, X. Cai, F. Sun, and J. Zhang, “Scene image classification method based on Alex-Net model,” 2016

3rd Int. Conf. Inf. Cybern. Comput. Soc. Syst. ICCSS 2016, pp. 363–367, 2016.

[13] J. Gao, J. Yang, G. Wang, and M. Li, “A novel feature extraction method for scene recognition based on

Centered Convolutional Restricted Boltzmann Machines,” Neurocomputing, vol. 214, pp. 708– 717, 2016.

[14] P. Tang, H. Wang, and S. Kwong, “G-MS2F: GoogLeNet based multi- stage feature fusion of deep CNN

for scene recognition,” Neurocomputing, vol. 225, no. November 2016, pp. 188–197, 2017.

[15] M. Sabokrou, M. Fayyaz, M. Fathy, and R. Klette, “Deep-Cascade: Cascading 3D Deep Neural Networks

for Fast Anomaly Detection and Localization in Crowded Scenes,” IEEE Trans. Image Process., vol. 26,

no. 4, pp. 1992–2004, 2017.

[16] Khosla, R. Uhlenbrock, and Y. Chen, “Automated scene understanding via fusion of image and object

features,” 2017 IEEE Int. Symp. Technol. Homel. Secur. HST 2017, pp. 15–18, 2017.

[17] Ondieki, “Convolutional Neural Networks for Scene Recognition,” pp. 2–8.

[18] T. Zhang and Q. Wang, “Deep Learning Based Feature Selection for Remote Sensing Scene Classificatio,”

IEEE Geosci. Remote Sens. Lett., vol. 12, no. 11, pp. 1–5, 2015.

[19] Helou and C. Nguyen, “Unsupervised Deep Learning for Scene Recognition,” pp. 1–10, 2011.

[20] W. Zhong, “Movie scene recognition with Convolutional Neural Networks,” 2015.

[21] X. Lu, X. Li, and L. Mou, “Semi-supervised multitask learning for scene recognition,” IEEE Trans.

Cybern., vol. 45, no. 9, pp. 1967– 1976, 2015.

Downloads

Published

18.09.2024

How to Cite

Shri Kumar, B., Naren, J., Vithya, G., & Prahathish, K. (2024). A Novel Architecture based on Deep Learning for Scene Image Recognition. International Journal of Psychosocial Rehabilitation, 23(1), 400-404. https://doi.org/10.61841/t5fynz19